{"id":13443657,"url":"https://github.com/open-mmlab/mmtracking","last_synced_at":"2025-05-14T03:08:28.370Z","repository":{"id":37366303,"uuid":"291213368","full_name":"open-mmlab/mmtracking","owner":"open-mmlab","description":"OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.","archived":false,"fork":false,"pushed_at":"2023-09-19T07:31:38.000Z","size":2984,"stargazers_count":3697,"open_issues_count":274,"forks_count":599,"subscribers_count":48,"default_branch":"master","last_synced_at":"2025-04-13T05:12:42.837Z","etag":null,"topics":["multi-object-tracking","single-object-tracking","tracking","video-instance-segmentation","video-object-detection"],"latest_commit_sha":null,"homepage":"https://mmtracking.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/open-mmlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-08-29T06:16:56.000Z","updated_at":"2025-04-11T02:05:34.000Z","dependencies_parsed_at":"2022-07-09T20:30:36.893Z","dependency_job_id":"35eebf79-08f1-4b77-9cfc-c9cf1a71b38d","html_url":"https://github.com/open-mmlab/mmtracking","commit_stats":{"total_commits":308,"total_committers":41,"mean_commits":7.512195121951219,"dds":0.7727272727272727,"last_synced_commit":"5b47f18054d9b9aae3cb5eb24618f8a3ac20c79e"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmtracking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmtracking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmtracking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmtracking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/open-mmlab","download_url":"https://codeload.github.com/open-mmlab/mmtracking/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254059507,"owners_count":22007768,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["multi-object-tracking","single-object-tracking","tracking","video-instance-segmentation","video-object-detection"],"created_at":"2024-07-31T03:02:06.425Z","updated_at":"2025-05-14T03:08:28.332Z","avatar_url":"https://github.com/open-mmlab.png","language":"Python","funding_links":[],"categories":["Python","Computer Vision","Repos","工具箱","Frameworks"],"sub_categories":["General Purpose CV","MOT20"],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"resources/mmtrack-logo.png\" width=\"600\"/\u003e\n  \u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n  \u003cdiv align=\"center\"\u003e\n    \u003cb\u003e\u003cfont size=\"5\"\u003eOpenMMLab website\u003c/font\u003e\u003c/b\u003e\n    \u003csup\u003e\n      \u003ca href=\"https://openmmlab.com\"\u003e\n        \u003ci\u003e\u003cfont size=\"4\"\u003eHOT\u003c/font\u003e\u003c/i\u003e\n      \u003c/a\u003e\n    \u003c/sup\u003e\n    \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\n    \u003cb\u003e\u003cfont size=\"5\"\u003eOpenMMLab platform\u003c/font\u003e\u003c/b\u003e\n    \u003csup\u003e\n      \u003ca href=\"https://platform.openmmlab.com\"\u003e\n        \u003ci\u003e\u003cfont size=\"4\"\u003eTRY IT OUT\u003c/font\u003e\u003c/i\u003e\n      \u003c/a\u003e\n    \u003c/sup\u003e\n  \u003c/div\u003e\n  \u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmtrack)](https://pypi.org/project/mmtrack/)\n[![PyPI](https://img.shields.io/pypi/v/mmtrack)](https://pypi.org/project/mmtrack)\n[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmtracking.readthedocs.io/en/latest/)\n[![badge](https://github.com/open-mmlab/mmtracking/workflows/build/badge.svg)](https://github.com/open-mmlab/mmtracking/actions)\n[![codecov](https://codecov.io/gh/open-mmlab/mmtracking/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmtracking)\n[![license](https://img.shields.io/github/license/open-mmlab/mmtracking.svg)](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE)\n\n[📘Documentation](https://mmtracking.readthedocs.io/) |\n[🛠️Installation](https://mmtracking.readthedocs.io/en/latest/install.html) |\n[👀Model Zoo](https://mmtracking.readthedocs.io/en/latest/model_zoo.html) |\n[🆕Update News](https://mmtracking.readthedocs.io/en/latest/changelog.html) |\n[🤔Reporting Issues](https://github.com/open-mmlab/mmtracking/issues/new/choose)\n\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\nEnglish | [简体中文](README_zh-CN.md)\n\n\u003c/div\u003e\n\n## Introduction\n\nMMTracking is an open source video perception toolbox by PyTorch. It is a part of [OpenMMLab](https://openmmlab.com) project.\n\nThe master branch works with **PyTorch1.5+**.\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/24663779/103343312-c724f480-4ac6-11eb-9c22-b56f1902584e.gif\" width=\"800\"/\u003e\n\u003c/div\u003e\n\n### Major features\n\n- **The First Unified Video Perception Platform**\n\n  We are the first open source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation.\n\n- **Modular Design**\n\n  We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules.\n\n- **Simple, Fast and Strong**\n\n  **Simple**: MMTracking interacts with other OpenMMLab projects. It is built upon [MMDetection](https://github.com/open-mmlab/mmdetection) that we can capitalize any detector only through modifying the configs.\n\n  **Fast**: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations.\n\n  **Strong**: We reproduce state-of-the-art models and some of them even outperform the official implementations.\n\n## What's New\n\nWe release MMTracking 1.0.0rc0, the first version of MMTracking 1.x.\n\nBuilt upon the new [training engine](https://github.com/open-mmlab/mmengine), MMTracking 1.x unifies the interfaces of datasets, models, evaluation, and visualization.\n\nWe also support more methods in MMTracking 1.x, such as [StrongSORT](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/mot/strongsort) for MOT, [Mask2Former](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/vis/mask2former) for VIS, [PrDiMP](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/sot/prdimp) for SOT.\n\nPlease refer to [dev-1.x](https://github.com/open-mmlab/mmtracking/tree/dev-1.x) branch for the using of MMTracking 1.x.\n\n## Installation\n\nPlease refer to [install.md](docs/en/install.md) for install instructions.\n\n## Getting Started\n\nPlease see [dataset.md](docs/en/dataset.md) and [quick_run.md](docs/en/quick_run.md) for the basic usage of MMTracking.\n\nA Colab tutorial is provided. You may preview the notebook [here](./demo/MMTracking_Tutorial.ipynb) or directly run it on [Colab](https://colab.research.google.com/github/open-mmlab/mmtracking/blob/master/demo/MMTracking_Tutorial.ipynb).\n\nThere are also usage [tutorials](docs/en/tutorials/), such as [learning about configs](docs/en/tutorials/config.md), [an example about detailed description of vid config](docs/en/tutorials/config_vid.md), [an example about detailed description of mot config](docs/en/tutorials/config_mot.md), [an example about detailed description of sot config](docs/en/tutorials/config_sot.md), [customizing dataset](docs/en/tutorials/customize_dataset.md), [customizing data pipeline](docs/en/tutorials/customize_data_pipeline.md), [customizing vid model](docs/en/tutorials/customize_vid_model.md), [customizing mot model](docs/en/tutorials/customize_mot_model.md), [customizing sot model](docs/en/tutorials/customize_sot_model.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [useful tools](docs/en/useful_tools_scripts.md).\n\n## Benchmark and model zoo\n\nResults and models are available in the [model zoo](docs/en/model_zoo.md).\n\n### Video Object Detection\n\nSupported Methods\n\n- [x] [DFF](configs/vid/dff) (CVPR 2017)\n- [x] [FGFA](configs/vid/fgfa) (ICCV 2017)\n- [x] [SELSA](configs/vid/selsa) (ICCV 2019)\n- [x] [Temporal RoI Align](configs/vid/temporal_roi_align) (AAAI 2021)\n\nSupported Datasets\n\n- [x] [ILSVRC](http://image-net.org/challenges/LSVRC/2017/)\n\n### Single Object Tracking\n\nSupported Methods\n\n- [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019)\n- [x] [STARK](configs/sot/stark) (ICCV 2021)\n- [x] [MixFormer](configs/sot/mixformer) (CVPR 2022)\n- [ ] [PrDiMP](https://arxiv.org/abs/2003.12565) (CVPR2020) (WIP)\n\nSupported Datasets\n\n- [x] [LaSOT](http://vision.cs.stonybrook.edu/~lasot/)\n- [x] [UAV123](https://cemse.kaust.edu.sa/ivul/uav123/)\n- [x] [TrackingNet](https://tracking-net.org/)\n- [x] [OTB100](http://www.visual-tracking.net/)\n- [x] [GOT10k](http://got-10k.aitestunion.com/)\n- [x] [VOT2018](https://www.votchallenge.net/vot2018/)\n\n### Multi-Object Tracking\n\nSupported Methods\n\n- [x] [SORT/DeepSORT](configs/mot/deepsort) (ICIP 2016/2017)\n- [x] [Tracktor](configs/mot/tracktor) (ICCV 2019)\n- [x] [QDTrack](configs/mot/qdtrack) (CVPR 2021)\n- [x] [ByteTrack](configs/mot/bytetrack) (ECCV 2022)\n- [x] [OC-SORT](configs/mot/ocsort) (arXiv 2022)\n\nSupported Datasets\n\n- [x] [MOT Challenge](https://motchallenge.net/)\n- [x] [CrowdHuman](https://www.crowdhuman.org/)\n- [x] [LVIS](https://www.lvisdataset.org/)\n- [x] [TAO](https://taodataset.org/)\n- [x] [DanceTrack](https://arxiv.org/abs/2111.14690)\n\n### Video Instance Segmentation\n\nSupported Methods\n\n- [x] [MaskTrack R-CNN](configs/vis/masktrack_rcnn) (ICCV 2019)\n\nSupported Datasets\n\n- [x] [YouTube-VIS](https://youtube-vos.org/dataset/vis/)\n\n## Contributing\n\nWe appreciate all contributions to improve MMTracking. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) for the contributing guideline and [this discussion](https://github.com/open-mmlab/mmtracking/issues/73) for development roadmap.\n\n## Acknowledgement\n\nMMTracking is an open source project that welcome any contribution and feedback.\nWe wish that the toolbox and benchmark could serve the growing research\ncommunity by providing a flexible as well as standardized toolkit to reimplement existing methods\nand develop their own new video perception methods.\n\n## Citation\n\nIf you find this project useful in your research, please consider cite:\n\n```latex\n@misc{mmtrack2020,\n    title={{MMTracking: OpenMMLab} video perception toolbox and benchmark},\n    author={MMTracking Contributors},\n    howpublished = {\\url{https://github.com/open-mmlab/mmtracking}},\n    year={2020}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in OpenMMLab\n\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\n- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning Toolbox and Benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab Model Compression Toolbox and Benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab FewShot Learning Toolbox and Benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration):  OpenMMLab Generative Model toolbox and benchmark.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMlab deep learning model deployment toolset.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-mmlab%2Fmmtracking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopen-mmlab%2Fmmtracking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-mmlab%2Fmmtracking/lists"}